Open Machine Learning Course
Topics: #EDA, #Visualization, #Classification, #Regression, #Ensembles, #FeatureEngineering, #Clustering, #OnlineLearning, #TimeSeries, #GradientBoosting
mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky, Ph.D.. Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. Thus, the course meets you with math formulae in lectures, and a lot of practice in a form of assignments and Kaggle Inclass competitions. Currently, the course is in a self-paced mode. Here we guide you through the self-paced mlcourse.ai.
#armcourses #armknowledgesharing
Topics: #EDA, #Visualization, #Classification, #Regression, #Ensembles, #FeatureEngineering, #Clustering, #OnlineLearning, #TimeSeries, #GradientBoosting
mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky, Ph.D.. Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. Thus, the course meets you with math formulae in lectures, and a lot of practice in a form of assignments and Kaggle Inclass competitions. Currently, the course is in a self-paced mode. Here we guide you through the self-paced mlcourse.ai.
#armcourses #armknowledgesharing
An Introduction to Statistical Learning with applications in PYTHON!
As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning.
The Python edition (ISLP) was published in 2023.
The chapters cover the following topics:
- What is statistical learning?
- Regression
- Classification
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Support vector machines
- Deep learning
- Survival analysis
- Unsupervised learning
- Multiple testing
Link: https://www.statlearning.com
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #ISLR #ISLP #regression #classification #resampling #linearmodels #regularization #trees #svm #deeplearning #unsupervisedlearning #abtesting
@data_science_weekly
As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning.
The Python edition (ISLP) was published in 2023.
The chapters cover the following topics:
- What is statistical learning?
- Regression
- Classification
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Support vector machines
- Deep learning
- Survival analysis
- Unsupervised learning
- Multiple testing
Link: https://www.statlearning.com
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #ISLR #ISLP #regression #classification #resampling #linearmodels #regularization #trees #svm #deeplearning #unsupervisedlearning #abtesting
@data_science_weekly
Machine Learning for Everyone. In simple words. With real-world examples. Yes, again.
Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.
A simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone. Whether you are a programmer or a manager.
Link: https://vas3k.com/blog/machine_learning/
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #ml #machinelearning #data #features #algorithms #classification #regression #neuralnets #deeplearning #dl #supervised #unsupervised
@data_science_weekly
Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.
A simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone. Whether you are a programmer or a manager.
Link: https://vas3k.com/blog/machine_learning/
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #ml #machinelearning #data #features #algorithms #classification #regression #neuralnets #deeplearning #dl #supervised #unsupervised
@data_science_weekly
Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis by Ethan Bueno de Mesquita, Anthony Fowler
An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives.
Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data.
- An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields
- Introduces the basic toolkit of data analysis―including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity
- Uses real-world examples and data from a wide variety of subjects
- Includes practice questions and data exercises
Link: https://www.amazon.com/Thinking-Clearly-Data-Quantitative-Reasoning/dp/0691214352
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #datascience #correlation #regression #causation #randomizedexperiments #statistics
@data_science_weekly
An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives.
Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data.
- An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields
- Introduces the basic toolkit of data analysis―including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity
- Uses real-world examples and data from a wide variety of subjects
- Includes practice questions and data exercises
Link: https://www.amazon.com/Thinking-Clearly-Data-Quantitative-Reasoning/dp/0691214352
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #datascience #correlation #regression #causation #randomizedexperiments #statistics
@data_science_weekly